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Toward a generalized notion of discrete time for modeling temporal networks

Published online by Cambridge University Press:  25 January 2022

Konstantin Kueffner
Vienna University of Economics and Business, WU, Vienna, Austria Secure Business Austria Research Center (SBA), Vienna, Austria
Mark Strembeck*
Vienna University of Economics and Business, WU, Vienna, Austria Secure Business Austria Research Center (SBA), Vienna, Austria Complexity Science Hub Vienna (CSH), Vienna, Austria
*Corresponding author. Email:
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Many real-world networks, including social networks and computer networks for example, are temporal networks. This means that the vertices and edges change over time. However, most approaches for modeling and analyzing temporal networks do not explicitly discuss the underlying notion of time. In this paper, we therefore introduce a generalized notion of discrete time for modeling temporal networks. Our approach also allows for considering nondeterministic time and incomplete data, two issues that are often found when analyzing datasets extracted from online social networks, for example. In order to demonstrate the consequences of our generalized notion of time, we also discuss the implications for the computation of (shortest) temporal paths in temporal networks. In addition, we implemented an R-package that provides programming support for all concepts discussed in this paper. The R-package is publicly available for download.

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© The Author(s), 2022. Published by Cambridge University Press


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